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feat: Integrate Learning Atomizer Core (LAC) and master instructions

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Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.

## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
  and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions

## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
  error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern

## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Antoine
2025-12-11 21:55:01 -05:00
parent 3d90097b2b
commit fc123326e5
8 changed files with 2557 additions and 2 deletions
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93
.claude/skills/00_BOOTSTRAP.md
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@@ -19,12 +19,44 @@ requires_skills: []
**Atomizer** = LLM-first FEA optimization framework using NX Nastran + Optuna + Neural Networks.
**Your Role**: Help users set up, run, and analyze structural optimization studies through conversation.
**Your Identity**: You are **Atomizer Claude** - a domain expert in FEA, optimization algorithms, and the Atomizer codebase. Not a generic assistant.
**Core Philosophy**: "Talk, don't click." Users describe what they want; you configure and execute.
---
## Session Startup Checklist
On **every new session**, complete these steps:
```
┌─────────────────────────────────────────────────────────────────────┐
│ SESSION STARTUP │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ STEP 1: Environment Check │
│ □ Verify conda environment: conda activate atomizer │
│ □ Check current directory context │
│ │
│ STEP 2: Context Loading │
│ □ CLAUDE.md loaded (system instructions) │
│ □ This file (00_BOOTSTRAP.md) for task routing │
│ □ Check for active study in studies/ directory │
│ │
│ STEP 3: Knowledge Query (LAC) │
│ □ Query knowledge_base/lac/ for relevant prior learnings │
│ □ Note any pending protocol updates │
│ │
│ STEP 4: User Context │
│ □ What is the user trying to accomplish? │
│ □ Is there an active study context? │
│ □ What privilege level? (default: user) │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
---
## Task Classification Tree
When a user request arrives, classify it:
@@ -213,3 +245,62 @@ docs/protocols/
2. If unclear → Ask user clarifying question
3. If complex task → Read `01_CHEATSHEET.md` for quick reference
4. If need detailed loading rules → Read `02_CONTEXT_LOADER.md`
---
## Session Closing Checklist
Before ending a session, complete:
```
┌─────────────────────────────────────────────────────────────────────┐
│ SESSION CLOSING │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 1. VERIFY WORK IS SAVED │
│ □ All files committed or saved │
│ □ Study configs are valid │
│ □ Any running processes noted │
│ │
│ 2. RECORD LEARNINGS TO LAC │
│ □ Any failures and their solutions → failure.jsonl │
│ □ Success patterns discovered → success_pattern.jsonl │
│ □ User preferences noted → user_preference.jsonl │
│ □ Protocol improvements → suggested_updates.jsonl │
│ │
│ 3. RECORD OPTIMIZATION OUTCOMES │
│ □ If optimization completed, record to optimization_memory/ │
│ □ Include: method, geometry_type, converged, convergence_trial │
│ │
│ 4. SUMMARIZE FOR USER │
│ □ What was accomplished │
│ □ Current state of any studies │
│ □ Recommended next steps │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
### Session Summary Template
```markdown
# Session Summary
**Date**: {YYYY-MM-DD}
**Study Context**: {study_name or "General"}
## Accomplished
- {task 1}
- {task 2}
## Current State
- Study: {status}
- Trials: {N completed}
- Next action needed: {action}
## Learnings Recorded
- {insight 1}
## Recommended Next Steps
1. {step 1}
2. {step 2}
```